Pith. sign in

REVIEW 6 major objections 5 minor 56 references

A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression

T0 review · 6 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This survey argues that regression models and classification models answer different predictive-maintenance questions, and that each is better suited to its own.

desk verdict A survey with a sensible organizing frame but a broken evidentiary base: the placeholder reference alone makes the comparative claims unauditable. read the letter →

arxiv 2506.20090 v1 pith:D5C5W24I submitted 2025-06-25 cs.LG

Ainaz Jamshidi , Dongchan Kim , Muhammad Arif This is my paper · ORCID
classification cs.LG
keywords predictivemaintenanceremainingusefullifeprognosticsregressionclassificationmachinelearningdeepdataimbalance
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper reviews predictive maintenance (PdM) literature and aims to give the first standalone comparison of the two main prognostics model families. It claims regression-based methods are the natural choice when the goal is remaining useful life (RUL), while classification-based methods are better when the goal is to predict whether failure will occur inside a defined time horizon. The survey reads the literature through this distinction, organizes representative studies into the two camps, and draws out recurring obstacles such as data imbalance and high-dimensional feature spaces. A sympathetic reader would take away that method choice should follow the maintenance decision to be supported rather than the model family.

What carries the argument

The machinery that carries the argument is a taxonomy of two prognostics tasks. Regression predicts remaining useful life (RUL), either directly from a degradation index or via short-term time-series forecasting with threshold-based post-processing; classification predicts the probability of failure within a specified time window. The survey sorts the reviewed studies into these two camps and uses the distinction to explain why certain models appear in certain contexts—for example, deep convolutional and LSTM networks in regression-based RUL prediction, and cost-sensitive classifiers in failure-window prediction. This taxonomy is what lets the paper claim the two families are complementary rather than competing.

What would settle it

Run regression and classification models on the same public turbofan-degradation benchmark, scoring both RUL error and failure-window accuracy. If a classification model converted to expected time-to-failure matches or beats dedicated regression models on RUL error, the paper's claim that regression is better suited to RUL prediction fails.

Watch

Extended reading notes

Core claim

The paper's central claim is that no existing survey compares regression- and classification-based PdM approaches on their own terms, and that doing so yields a clear division of labor. Regression formulations—direct RUL regression on a degradation index or time-series forecasting followed by thresholding—give operators a concrete estimate of remaining life, which is why the survey finds them dominant in RUL prediction. Classification formulations predict the probability of failure over a future time window, which the survey finds more aligned with maintenance actions that must be scheduled ahead of time. The survey concludes that each paradigm has distinct strengths and that hybrid models combining both are a promising direction.

Load-bearing premise

The survey's conclusions rest entirely on the reality and accurate summarization of the papers it cites, and reference [15], with placeholder authors, shows this premise cannot be taken for granted.

Editorial extensions

If this is right

  • Practitioners who need a number for time-left-before-failure should select a regression-based RUL model, which yields a directly interpretable output.
  • Practitioners who need to decide whether to intervene within a fixed horizon should use a classification model, whose output is a failure probability for that window.
  • The reported obstacles of data imbalance and high dimensionality mean classification-based PdM needs cost-sensitive training, resampling, and dimensionality reduction to work in practice.
  • Hybrid approaches that combine regression and classification are identified as an emerging trend and a route to more robust maintenance systems.
  • Future progress depends on standardized public datasets and benchmarking platforms, which the paper leaves to subsequent work.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Implicit in the survey but not stated: a classifier's failure-window probabilities can be converted into an RUL estimate, and an RUL regression can be thresholded into a failure window, so a benchmark comparing both on the same dataset would directly test the claimed division of labor.
  • If the division of labor holds, model selection in PdM should be driven by the maintenance decision horizon—short fixed intervals favor classification, open-ended planning favors regression—rather than by raw accuracy.
  • The survey's emphasis on data imbalance suggests that classification-based PdM may benefit more from imbalanced-learning techniques than from new architectures, a direction the paper only sketches.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

6 major / 5 minor

Summary. This manuscript presents a survey of predictive maintenance (PdM) methods, with a focus on comparing regression-based and classification-based prognostics. The paper reviews maintenance strategies, discusses two regression workflows (direct RUL prediction and time-series forecasting), and surveys classification-based methods that predict failure within a time horizon. A central claim is that regression methods are better suited for remaining useful life (RUL) prediction while classification methods are better for predicting failure within a defined horizon, and the abstract asserts that this is the first standalone comparative study of these two families.

Significance. The topic is timely and the proposed regression-versus-classification framing is a useful organizing principle for practitioners and researchers. The paper covers a diverse set of methods and identifies relevant challenges such as data imbalance and high-dimensional feature spaces. However, the survey's reliability is seriously compromised by citation errors, an unsupported novelty claim, and the absence of a systematic methodology. As presented, the comparative conclusions cannot be independently audited, so the significance is currently more in the framing than in the evidence assembled.

major comments (6)
  1. [References and §1.1, §2.3, §2.4] Reference [15] is a placeholder: the bibliography lists 'Jane Doe and John Smith' as authors. This entry is cited in §1.1, §2.3, and §2.4 to support statements about PdM in the automotive sector and hybrid maintenance. A survey cannot base its claims on an unverifiable source; the authors must replace this with a genuine citation or remove the associated claims.
  2. [References [10] and [22]] References [10] and [22] are the same paper (Lima et al., 'Smart predictive maintenance for high-performance computing systems: a literature review', The Journal of Supercomputing, 2021), listed with slightly different bibliographic formatting. Duplicate entries create ambiguity about whether distinct sources support distinct claims, and the bibliography must be de-duplicated.
  3. [Table 3] Table 3 misattributes multiple entries. The row labeled 'Tran et al. (2012) [37]' corresponds to bibliography entry [37], which is by Pham et al. (2012), not Tran et al. The row 'Zhang et al. (2017) MODBNE' is assigned reference [44], but [44] in the bibliography is Zheng et al. (2017) LSTM; the MODBNE work is actually cited as [53] in §3.1. These errors prevent readers from tracing the table's entries to their sources, undermining the survey's evidentiary foundation.
  4. [§3.2 and §3.2.1, Table 3, Reference [48]] The study by Prytz et al. is dated inconsistently: Table 3 and §3.2 say 'Prytz et al. (2018)', while §3.2.1 and the bibliography [48] say 'Prytz et al. (2013)'. This inconsistency affects the chronology of the reviewed methods and further reduces confidence in the presentation.
  5. [Abstract and §1.2] The claim that 'there has not yet been a standalone comparative study between regression- and classification-based approaches' is a strong novelty assertion. The paper does not describe a systematic search protocol, and the abstract itself identifies a 'systematic review' as future work. Without any evidence of a comprehensive literature search, the 'first' claim is unsubstantiated and should be either removed or supported by a documented search strategy.
  6. [§3.1.1] The statement 'Most studies employed logistic regression to transfer the raw condition monitoring data to the failure probabilities' is not supported by Table 3, where only Yan et al. (2004) [23] explicitly uses logistic regression; most listed studies use ANNs, SVMs, or deep networks. This overgeneralization misrepresents the surveyed literature and should be corrected.
minor comments (5)
  1. [Title] The title contains 'A N Analysis' which appears to be a spacing/typing error; it should presumably read 'An Analysis'.
  2. [§1.1] The sentence 'The latest trend in predicting maintenance is, in fact, defined by Industry' appears to be missing '4.0' after 'Industry'.
  3. [§3.1] The phrase 'an support vector machine' should be 'a support vector machine'.
  4. [Table 1 and Table 3] Acronyms such as WPD (wavelet packet decomposition) and MODBNE (multiobjective deep belief network ensemble) are used in Table 3 but are not defined in the nomenclature list in Table 1.
  5. [§4] The conclusion states that 'Regression processes generally outperform better for RUL predictions'; the phrase 'outperform better' is redundant and should be rephrased.

Circularity Check

1 steps flagged · score 4.0 of 10

The paper's main comparative conclusion restates its own taxonomy: regression-based methods are defined by RUL output and classification-based methods by failure-window output, so the conclusion that each is best for its own target holds by construction.

  1. self definitional [Section 3.1, Section 3.2, Section 4 (Conclusion), and Table 3 'Output Type' column]
    "In PdM problems, regression-based formulations are commonly used to predict the RUL of equipment [52]... The first approach is calculating the value of RUL directly via feeding the data into a machine learning algorithm [29]... In this section, we aim to survey studies that aimed to predict the time window in which failures would occur using ML..."

    The paper builds its two categories by prediction target: Section 3.1 defines regression-based PdM as RUL prediction, and Section 3.2 defines classification-based PdM as predicting the failure time window. The 'Output Type' column of Table 3 assigns RUL to the regression rows and time-window to the classification rows. The conclusion then restates those definitions as a comparative finding: regression is better for RUL because it outputs RUL, and classification is better for a time horizon because it outputs a time window. No head-to-head empirical comparison or benchmark is provided to establish 'better'; the claim is true by construction because the categories were formed from the output types.

full rationale

The paper is a literature survey, not a derivation-based study, so most circularity patterns do not apply: there are no fitted parameters, no quantitative predictions, no self-citations by the authors, and no imported uniqueness theorems. The placeholder reference '[15] Jane Doe and John Smith' and the cited-reference mismatches are serious reference-integrity problems, but they are correctness and traceability concerns rather than circularity. The one load-bearing circular step is the comparative conclusion: regression-based and classification-based methods are defined by their output types, and the conclusion that each family is best suited to its own output type is a restatement of that definition. The rest of the survey does summarize external literature, and the novelty claim is an external historical assertion, so the circularity is partial rather than total; a corrected, evidence-based comparison would require head-to-head studies of both families on the same tasks, which the paper does not provide.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The central claim rests on three unproven assumptions about the survey's own basis: that the cited papers are real and accurately summarized, that the regression/classification dichotomy is a valid organizing frame, and that the paper selection is representative. The first assumption is observably violated by the placeholder citation ref [15] and duplicated refs [10] and [22].

assumptions (3)
  • domain assumption All cited references represent real published works and are accurately summarized.
    The survey's evidentiary value depends on the accuracy of its reference list; the presence of a placeholder citation (ref [15], 'Jane Doe and John Smith') and duplicated references ([10] and [22]) indicates this assumption is violated.
  • domain assumption Regression and classification form a meaningful dichotomy for organizing PdM prognostics.
    The paper's comparative framing and its conclusion that regression suits RUL prediction while classification suits failure-window prediction depend on this categorization being coherent and exhaustive.
  • domain assumption The selection of surveyed papers is representative of the PdM literature.
    Section 1.1 describes no systematic search or inclusion criteria, so representativeness is asserted rather than demonstrated.

how reviews work

0 comments
Cite this review

Pith. "Pith review of A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression." pith.science (2026). https://pith.science/paper/D5C5W24I

@misc{pith2026250620090,
  author       = {Pith},
  title        = {Pith review of: A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/D5C5W24I}},
  note         = {Machine review of arXiv:2506.20090}
}
read the original abstract

Predictive maintenance (PdM) has become a crucial element of modern industrial practice. PdM plays a significant role in operational dependability and cost management by decreasing unforeseen downtime and optimizing asset life cycle management. Machine learning and deep learning have enabled more precise forecasts of equipment failure and remaining useful life (RUL). Although many studies have been conducted on PdM, there has not yet been a standalone comparative study between regression- and classification-based approaches. In this review, we look across a range of PdM methodologies, while focusing more strongly on the comparative use of classification and regression methods in prognostics. While regression-based methods typically provide estimates of RUL, classification-based methods present a forecast of the probability of failure across defined time intervals. Through a comprehensive analysis of recent literature, we highlight key advancements, challenges-such as data imbalance and high-dimensional feature spaces-and emerging trends, including hybrid approaches and AI-enabled prognostic systems. This review aims to provide researchers and practitioners with an awareness of the strengths and compromises of various PdM methods and to help identify future research and build more robust, directed adaptive maintenance systems. Future work may include a systematic review of practical aspects such as public datasets, benchmarking platforms, and open-source tools to support the advancement of PdM research.

Figures

Figures reproduced from arXiv: 2506.20090 by the authors.

Figure 1
Figure 1. Comparison of maintenance strategies showing how costs decline and equipment availability rises as you [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Taxonomy of maintenance strategies, including corrective, preventive, predictive, and hybrid maintenance [ [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Evolution of major maintenance strategies. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: An overview of maintenance strategies structured around the prognostics taxonomy, highlighting method-level [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: A depiction of the sliding window method to prepare input time series data for machine learning and deep [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: Overview of two regression-based predictive-maintenance workflows: (1) direct regression of a degradation [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: High-level overview of utilizing machine learning in a predictive maintenance pipeline. [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

56 extracted references · 55 canonical work pages

  1. [15]

    Predictive maintenance in the automotive sector: A literature review

    Jane Doe and John Smith. Predictive maintenance in the automotive sector: A literature review. International Journal of Automotive Technology, 22(7):1365–1379, 2021

    work page 2021

  2. [10]

    Smart predictive maintenance for high-performance computing systems: A literature review

    André Luis da Cunha Dantas Lima, Vitor Moraes Aranha, Caio Jordão de Lima Carvalho, and Erick Gio- vani Sperandio Nascimento. Smart predictive maintenance for high-performance computing systems: A literature review. The Journal of Supercomputing, 77:13494–13513, 2021

    work page 2021

  3. [22]

    Smart predictive maintenance for high-performance computing systems: a literature review

    André Luis da Cunha Dantas Lima, Vitor Moraes Aranha, Caio Jordão de Lima Carvalho, and Erick Gio- vani Sperandio Nascimento. Smart predictive maintenance for high-performance computing systems: a literature review. The Journal of Supercomputing, 77(11):13494–13513, 2021

    work page 2021

  4. [44]

    Long short-term memory network for remaining useful life estimation

    Shuai Zheng, Kosta Ristovski, Ahmed Farahat, and Chetan Gupta. Long short-term memory network for remaining useful life estimation. In 2017 IEEE International Conference on Prognostics and Health Management (ICPHM) , pages 88–95. IEEE, 2017

    work page 2017

  5. [53]

    Multiobjective deep belief networks ensemble for remaining useful life estimation in prognostics

    Chong Zhang, Pin Lim, A Kai Qin, and Kay Chen Tan. Multiobjective deep belief networks ensemble for remaining useful life estimation in prognostics. IEEE transactions on neural networks and learning systems , 28(10):2306–2318, 2016

    work page 2016

  6. [48]

    Analysis of truck compressor failures based on logged vehicle data

    Rune Prytz, Sławomir Nowaczyk, Thorsteinn Rögnvaldsson, and Stefan Byttner. Analysis of truck compressor failures based on logged vehicle data. In 9th International Conference on Data Mining, Las V egas, Nevada, USA, July 22–25, 2013. CSREA Press, 2013

    work page 2013

  7. [37]

    Machine performance degradation assessment and remaining useful life prediction using proportional hazard model and support vector machine

    Hong Thom Pham, Bo-Suk Yang, Tan Tien Nguyen, et al. Machine performance degradation assessment and remaining useful life prediction using proportional hazard model and support vector machine. Mechanical Systems and Signal Processing, 32:320–330, 2012

    work page 2012

  8. [23]

    A prognostic algorithm for machine performance assessment and its application

    Jihong Yan, Muammer Koc, and Jay Lee. A prognostic algorithm for machine performance assessment and its application. Production Planning & Control, 15(8):796–801, 2004

    work page 2004

Show all 56 references
  1. [1]

    A review on machinery diagnostics and prognostics implementing condition-based maintenance

    Andrew KS Jardine, Daming Lin, and Dragan Banjevic. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical systems and signal processing , 20(7):1483–1510, 2006

    2006

  2. [2]

    An introduction to predictive maintenance

    R Keith Mobley. An introduction to predictive maintenance. Elsevier, 2002

    2002

  3. [3]

    Selecting an appropriate supervised machine learning algorithm for predictive maintenance

    Abdelfetah Ouadah, Leila Zemmouchi-Ghomari, and Nedjma Salhi. Selecting an appropriate supervised machine learning algorithm for predictive maintenance. The International Journal of Advanced Manufacturing Technology , 119, 04 2022

    2022

  4. [4]

    Study on predictive maintenance strategy.International Journal of u-and e-Service, Science and Technology, 9(4):295–300, 2015

    Hongxia Wang, Xiaohui Ye, and Ming Yin. Study on predictive maintenance strategy.International Journal of u-and e-Service, Science and Technology, 9(4):295–300, 2015. 10 A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression

    2015

  5. [5]

    Remaining useful life prediction based on deep learning: A survey

    Fuhui Wu, Qingbo Wu, Yusong Tan, and Xinghua Xu. Remaining useful life prediction based on deep learning: A survey. Sensors, 24(11):3454, 2024

    2024

  6. [6]

    Rul forecasting for wind turbine predictive maintenance based on deep learning

    Syed Shazaib Shah, Daoliang Tan, and Sah Chandan Kumar. Rul forecasting for wind turbine predictive maintenance based on deep learning. Heliyon, 10(20):e39268, 2024

    2024

  7. [7]

    A probabilistic estimation of remaining useful life from censored time-to-event data

    Christian Marius Lillelund, Fernando Pannullo, Morten Opprud Jakobsen, Manuel Morante, and Christian Fischer Pedersen. A probabilistic estimation of remaining useful life from censored time-to-event data. Expert Systems with Applications, 2024. in press; arXiv:2405.01614

    2024 arXiv

  8. [8]

    Enhancing predictive maintenance strategies for oil and gas equipment through ensemble learning modeling

    Mei Wang, Xuesong Su, Huifang Song, Yifei Wang, and Xinxin Yang. Enhancing predictive maintenance strategies for oil and gas equipment through ensemble learning modeling. Journal of Petroleum Exploration and Production Technology, 15(3):46–56, 2025

    2025

  9. [9]

    Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry

    Andreas Theissler, Judith Pérez-Velázquez, Marcel Kettelgerdes, and Gordon Elger. Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry. Reliability Engineering & System Safety, 215:107864, 2021

    2021

  10. [11]

    Predictive maintenance in the industry: A comparative study on deep learning-based remaining useful life estimation

    Luciano Lorenti, Davide Dalle Pezze, Jacopo Andreoli, Chiara Masiero, Natalie Gentner, Yao Yang, and Gian An- tonio Susto. Predictive maintenance in the industry: A comparative study on deep learning-based remaining useful life estimation. 2023 IEEE International Conference on...

    2023

  11. [12]

    A dynamic predictive maintenance approach using probabilistic deep learning for a fleet of multi-component systems.IEEE Transactions on Industrial Informatics, 2023

    Luca Biggio, Alexander Wieland, Manuel Arias Chao, Iason Kastanis, and Olga Fink. A dynamic predictive maintenance approach using probabilistic deep learning for a fleet of multi-component systems.IEEE Transactions on Industrial Informatics, 2023

    2023

  12. [13]

    Improve predictive maintenance through the application of artificial intelligence: A systematic review

    Anthony D Scaife. Improve predictive maintenance through the application of artificial intelligence: A systematic review. Results in Engineering, 21:101645, 2024

    2024

  13. [14]

    System-level predictive maintenance: review of research literature and gap analysis

    Kyle Miller and Artur Dubrawski. System-level predictive maintenance: review of research literature and gap analysis. arXiv preprint arXiv:2005.05239, 2020

    2005 arXiv

  14. [16]

    Deep learning models for predictive maintenance: A survey, comparison, challenges, and prospect

    Anh Nguyen, Li Wang, et al. Deep learning models for predictive maintenance: A survey, comparison, challenges, and prospect. IEEE Transactions on Neural Networks and Learning Systems , 31(4):1387–1399, 2020

    2020

  15. [17]

    Revolutionizing system reliability: The role of ai in predictive maintenance strategies

    Liam Brown, Emily Green, and Nathan White. Revolutionizing system reliability: The role of ai in predictive maintenance strategies. Reliability Engineering & System Safety , 202:1–12, 2024

    2024

  16. [18]

    Towards multi-model approaches to predictive maintenance: A systematic literature survey on diagnostics and prognostics

    Hyun Lee, Soo-Min Park, and Tae Kim. Towards multi-model approaches to predictive maintenance: A systematic literature survey on diagnostics and prognostics. Journal of Manufacturing Systems, 49:235–245, 2021

    2021

  17. [19]

    Predictive maintenance modelling for through-life engineering services

    Caxton Okoh, Rajkumar Roy, and Jorn Mehnen. Predictive maintenance modelling for through-life engineering services. Procedia CIRP, 59:196–201, 2017

    2017

  18. [20]

    Maintenance today and future trends

    Andrew Starr, Basim Al-Najjar, Kenneth Holmberg, Erkki Jantunen, Jim Bellew, and Alhussein Albarbar. Maintenance today and future trends. In E-maintenance, pages 5–37. Springer, 2010

    2010

  19. [21]

    Predictive maintenance, its implementation and latest trends

    Sule Selcuk. Predictive maintenance, its implementation and latest trends. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture , 231(9):1670–1679, 2017

    2017

  20. [24]

    Remaining useful life estimation of critical components with application to bearings

    Kamal Medjaher, Diego Alejandro Tobon-Mejia, and Noureddine Zerhouni. Remaining useful life estimation of critical components with application to bearings. IEEE Transactions on Reliability, 61(2):292–302, 2012

    2012

  21. [25]

    Current status of machine prognostics in condition-based maintenance: a review

    Ying Peng, Ming Dong, and Ming Jian Zuo. Current status of machine prognostics in condition-based maintenance: a review. The International Journal of Advanced Manufacturing Technology , 50(1-4):297–313, 2010

    2010

  22. [26]

    A survey of data-driven prognostics

    Mark Schwabacher. A survey of data-driven prognostics. In Infotech@ Aerospace, page 7002. 2005

    2005

  23. [27]

    A model to predict the residual life of rolling element bearings given monitored condition information to date

    Wenbin Wang. A model to predict the residual life of rolling element bearings given monitored condition information to date. IMA Journal of Management Mathematics , 13(1):3–16, 2002. 11 A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification a...

    2002

  24. [28]

    Time series prediction techniques for estimating remaining useful lifetime of cutting tool failure

    Siti Azirah Asmai, Burairah Hussin, Mokhtar Mohd Yusof, and Abdul Samad Shibghatullah. Time series prediction techniques for estimating remaining useful lifetime of cutting tool failure. system, 5:6, 2014

    2014

  25. [29]

    Remaining useful life prediction of grinding mill liners using an artificial neural network

    Farzaneh Ahmadzadeh and Jan Lundberg. Remaining useful life prediction of grinding mill liners using an artificial neural network. Minerals Engineering, 53:1–8, 2013

    2013

  26. [30]

    A practical approach to combine data mining and prognostics for improved predictive maintenance

    Abdellatif Bey-Temsamani, Marc Engels, Andy Motten, Steve Vandenplas, and Agusmian P Ompusunggu. A practical approach to combine data mining and prognostics for improved predictive maintenance. Data Min. Case Stud, 36, 2009

    2009

  27. [31]

    Time series data prediction using sliding window based rbf neural network

    HS Hota, Richa Handa, and AK Shrivas. Time series data prediction using sliding window based rbf neural network. International Journal of Computational Intelligence Research , 13(5):1145–1156, 2017

    2017

  28. [32]

    A comparison of three data-driven techniques for prognostics

    Kai Goebel, Bhaskar Saha, and Abhinav Saxena. A comparison of three data-driven techniques for prognostics. In 62nd meeting of the society for machinery failure prevention technology (mfpt) , pages 119–131, 2008

    2008

  29. [33]

    A data-driven failure prognostics method based on mixture of gaussians hidden markov models

    Diego Alejandro Tobon-Mejia, Kamal Medjaher, Noureddine Zerhouni, and Gerard Tripot. A data-driven failure prognostics method based on mixture of gaussians hidden markov models. IEEE Transactions on reliability , 61(2):491–503, 2012

    2012

  30. [34]

    A new framework for remaining useful life estimation using support vector machine classifierf

    C Louen, SX Ding, and C Kandler. A new framework for remaining useful life estimation using support vector machine classifierf. In Control and Fault-Tolerant Systems (SysTol), 2013 Conference on, pages 228–233. IEEE, 2013

    2013

  31. [35]

    Predictive maintenance decision using statistical linear regression and kernel methods

    Tung Le, Ming Luo, Junhong Zhou, and Hian L Chan. Predictive maintenance decision using statistical linear regression and kernel methods. In Emerging Technology and Factory Automation (ETF A), 2014 IEEE, pages 1–6. IEEE, 2014

    2014

  32. [36]

    A predictive maintenance system for epitaxy processes based on filtering and prediction techniques

    Gian Antonio Susto, Alessandro Beghi, and Cristina De Luca. A predictive maintenance system for epitaxy processes based on filtering and prediction techniques. IEEE Transactions on Semiconductor Manufacturing , 25(4):638–649, 2012

    2012

  33. [38]

    Feature signature prediction of a boring process using neural network modeling with confidence bounds

    Gang Yu, Hai Qiu, Dragan Djurdjanovic, and Jay Lee. Feature signature prediction of a boring process using neural network modeling with confidence bounds. The International Journal of Advanced Manufacturing Technology , 30(7-8):614–621, 2006

    2006

  34. [39]

    A predictive tool for remaining useful life estimation of rotating machinery components

    Haitao Liao, Hai Qiu, Jay Lee, Daming Lin, Dragan Banjevic, and Andrew Jardine. A predictive tool for remaining useful life estimation of rotating machinery components. In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Enginee...

    2005

  35. [40]

    Dealing with time-series data in predictive maintenance problems

    Gian Antonio Susto and Alessandro Beghi. Dealing with time-series data in predictive maintenance problems. In Emerging Technologies and Factory Automation (ETF A), 2016 IEEE 21st International Conference on, pages 1–4. IEEE, 2016

    2016

  36. [41]

    Remaining useful life estimation in prognostics using deep convolution neural networks

    Xiang Li, Qian Ding, and Jian-Qiao Sun. Remaining useful life estimation in prognostics using deep convolution neural networks. Reliability Engineering & System Safety , 172:1–11, 2018

    2018

  37. [42]

    Multi-bearing remaining useful life collaborative prediction: A deep learning approach

    Lei Ren, Jin Cui, Yaqiang Sun, and Xuejun Cheng. Multi-bearing remaining useful life collaborative prediction: A deep learning approach. Journal of Manufacturing Systems, 43:248–256, 2017

    2017

  38. [43]

    Deep convolutional neural network based regression approach for estimation of remaining useful life

    Giduthuri Sateesh Babu, Peilin Zhao, and Xiao-Li Li. Deep convolutional neural network based regression approach for estimation of remaining useful life. In International conference on database systems for advanced applications, pages 214–228. Springer, 2016

    2016

  39. [45]

    Fault diagnosis and remaining useful life estimation of aero engine using lstm neural network

    Mei Yuan, Yuting Wu, and Li Lin. Fault diagnosis and remaining useful life estimation of aero engine using lstm neural network. In 2016 IEEE International Conference on Aircraft Utility Systems (AUS) , pages 135–140. IEEE, 2016

    2016

  40. [46]

    Predicting computer system failures using support vector machines

    Errin W Fulp, Glenn A Fink, and Jereme N Haack. Predicting computer system failures using support vector machines. WASL, 8:5–5, 2008

    2008

  41. [47]

    Sequence based classification for predictive maintenance

    Julio Martinez, Christianne Dennison, and Zhengyi Lian. Sequence based classification for predictive maintenance. 12 A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression

  42. [49]

    A dissimilarity-based approach to predictive maintenance with application to hvac systems

    Riccardo Satta, Stefano Cavallari, Eraldo Pomponi, Daniele Grasselli, Davide Picheo, and Carlo Annis. A dissimilarity-based approach to predictive maintenance with application to hvac systems. arXiv preprint arXiv:1701.03633, 2017

    2017 arXiv

  43. [50]

    Machine learning for predictive maintenance: A multiple classifier approach

    Gian Antonio Susto, Andrea Schirru, Simone Pampuri, Seán McLoone, and Alessandro Beghi. Machine learning for predictive maintenance: A multiple classifier approach. IEEE Transactions on Industrial Informatics , 11(3):812–820, 2015

    2015

  44. [51]

    Predicting future machine failure from machine state using logistic regression

    Matthew Battifarano, David DeSmet, Achyuth Madabhushi, and Parth Nabar. Predicting future machine failure from machine state using logistic regression. arXiv preprint arXiv:1804.06022, 2018

    2018 arXiv

  45. [52]

    An explainable regression framework for predicting remaining useful life of machines

    Talhat Khan, Kashif Ahmad, Jebran Khan, Imran Khan, and Nasir Ahmad. An explainable regression framework for predicting remaining useful life of machines. arXiv preprint arXiv:2204.13574, 2022

    2022 arXiv

  46. [54]

    Predictive maintenance for aircraft engine using machine learning: Trends and challenges

    FA Adryan and KW Sastra. Predictive maintenance for aircraft engine using machine learning: Trends and challenges. International Journal of Aviation Science and Engineering-A VIA, 3(1), 2025

    2025

  47. [55]

    Addressing the curse of imbalanced training sets: one-sided selection

    Miroslav Kubat, Stan Matwin, et al. Addressing the curse of imbalanced training sets: one-sided selection. In Icml, volume 97, pages 179–186. Nashville, USA, 1997

    1997

  48. [56]

    Remaining useful life estimation

    Farzaneh Ahmadzadeh and Jan Lundberg. Remaining useful life estimation. International Journal of System Assurance Engineering and Management, 5(4):461–474, 2014. 13

    2014

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.